Unknown Mutual Coupling Research Articles

Robust angle estimation algorithm for MIMO radar based on tensor analysis and reduced-dimension subspace reconstruction

A robust angle estimation algorithm based on tensor analysis and reduced-dimension subspace reconstruction is proposed for the noncircular signals with unknown mutual coupling in MIMO radar. The proposed algorithm constructs a special tensor analysis to capture the non-circular characteristics and multi-dimensional structure of non-circular signals, and can eliminate the influence of mutual coupling in the tensor domain. The signal subspace is obtained by using high-order singular value decomposition (HOSVD), and the direction-of-departure (DOD) of the target is obtained by combining the real value subspace estimation of direction of arrival (DOA). In addition, according to the working principle and echo model of bistatic MIMO radar, combined with array signal processing, the angle estimation algorithm of reduced-dimension subspace reconstruction is proposed, which can effectively solve the angle estimation problem of bistatic MIMO radar, reduce the calculation cost, improve the robustness of the algorithm, and solve the problem that angle matching cannot be realized after dimension reduction. The simulation results show that the algorithm proposed in this paper has good performance in recognition.

Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling.

Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations.

DOA Estimation Using Fourth-Order Cumulants in Nested Arrays with Structured Imperfections.

Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants.

Cumulant-based DOA Estimation of Noncircular Signals Against Unknown Mutual Coupling.

To effectively find the direction of non-circular signals received by a uniform linear array (ULA) in the presence of non-negligible perturbations between array elements, i.e., mutual coupling, in colored noise, a direction of arrival (DOA) estimation approach in the context of high order statistics is proposed in this correspondence. Exploiting the non-circularity hidden behind a certain class of wireless communication signals to build up an augmented cumulant matrix, and carrying out a reformulation of the distorted steering vector to extract the angular information from the unknown mutual coupling, by exploiting the characteristic of mutual coupling, i.e., a limited operating range and an inverse relation of coupling effects to interspace, we develop a MUSIC-like estimator based on the rank-reduction (RARE) technique to directly determine directions of incident signals without mutual coupling compensation. Besides, we provide a solution to the problem of coherency between signals and mutual coupling between sensors co-existing, by selecting a middle sub-array to mitigate the undesirable effects and exploiting the rotation-invariant property to blindly separate the coherent signals into different groups to enhance the degrees of freedom. Compared with the existing robust DOA methods to the unknown mutual coupling under the framework of fourth-order cumulants (FOC), our work takes advantage of the larger virtual array and is able to resolve more signals due to greater degrees of freedom. Additionally, as the effective aperture is virtually extended, the developed estimator can achieve better performance under scenarios with high degree of mutual coupling between two sensors. Simulation results demonstrate the validity and efficiency of the proposed method.

Robust DOA Estimation Against Mutual Coupling With Nested Array

In this letter, we focus on the problem of direction-of-arrival (DOA) estimation with a nested array in the presence of unknown mutual coupling. Firstly, the measurement model of the nested array with mutual coupling is established, and the properties of the corresponding array covariance matrix are derived. Based on the obtained properties, the coarray output signal is then reconstructed with reduced mutual coupling. Finally, joint sparse recovery technique is employed to extract the DOAs. Our algorithm can achieve underdetermined DOA estimation with satisfactory performance under unknown mutual coupling. Numerical results demonstrate the effectiveness of the proposed algorithm.

DOA Estimation of Strictly Noncircular Sources in Wireless Sensor Array Network via Block Sparse Representation

In this article, the direction of arrival estimation (DOA) of strictly noncircular sources with unknown mutual coupling is considered for wireless sensor array network (WSAN), and then a joint weighted block sparse recovery algorithm based on weighted subspace fitting (WSF) is proposed for DOA estimation.. In the proposed method, two block sparse representation models associated with the WSF principle are firstly constructed to remove the influence of unknown mutual coupling. Then, combining the advantages of noncircularity, a joint weighted block sparse recovery scheme is proposed to estimate DOA, in which the noncircular MUSIC-like (NC MUSIC-Like) spectrum function is utilized to form a weighted matrix for enhancing the solutions sparsity. Finally, the desired DOAs can be achieved with the help of the reconstructed block sparse matrix. Extensive experiments are simulated to verify that the proposed method can achieve superior estimation performance under the condition of unknown mutual coupling.

An Intelligent Deep Learning-Based Direction-of-Arrival Estimation Scheme Using Spherical Antenna Array With Unknown Mutual Coupling

Spherical antenna array (SAA) has become highly attractive where hemispherical scan coverage is required as it can provide uniform directivity in all the scan directions. Various direction-of-arrival (DoA) estimation methods suffer from different problems, such as low accuracies in mismatched conditions, high computational complexity and poor estimation in a harsh environment. Another critical concern is mutual coupling (MC) characteristics between the array elements. These problems affect the quality of the navigation signal in harsh environments. This paper presents a robust DoA estimation and mutual coupling compensation technique based on convolutional neural network (CNN) for Spherical Array. Spherical harmonic decomposition (SHD) is used to facilitate feature extraction in two sets, which contains different features about the elevation and azimuth of the source for DoA estimation. The features serve as input to the learning technique for separate estimation of elevation and azimuth, which consequently reduce computational complexity as against the joint estimation of DoA. Learning methods for DoA estimation with few frames and dense search grids within the spherical array configuration are presented. To solve the MC error, the DoA estimation scheme is also used to obtain accurate spectrum peak in the multipath scenario with unknown MC and sharper spectrum peak via the unique structure of the MC matrix and spatial smoothing algorithms. In all, experimental results, which is the ground truth to test any procedure, show the effectiveness, validity, and potential practical application of the proposed technique.

Impact of Mutual Coupling on Power-Domain Non-Orthogonal Multiple Access (NOMA)

This paper investigates the impact of mutual coupling (MC) on the achievable sum-rate of power-domain non-orthogonal multiple access (NOMA) system in both uplink (UL) and downlink (DL) transmissions. We assume a single-antenna user NOMA system with directional beamforming in the millimeter wave (mmW) channel. Due to the electromagnetic interaction between the antenna elements, called mutual coupling, at the base station (BS), the steering vector of the channel is affected. Consequently, this leads to distorted antenna pattern pointing towards different directions and reduction in channel gain, based on antenna array structure. In this paper, different antenna geometries and configurations, such as one-dimensional (1D) uniform linear array (ULA), two-dimensional (2D), and three-dimensional (3D) uniform circular array (UCA), are implemented at the BS, where induced electromotive force (EMF) and method of moment (MOM) techniques are used to generate the MC coefficients. We first examine the antenna patterns affected by MC, followed by channel gains of user terminals (UT)s. Then sum-rate of the NOMA system is modified in the presence of MC. Furthermore, mutual coupling is compensated successfully for both UL and DL systems for the given antenna structures. Simulation results show that mutual coupling degrades the sum-rate performance of the NOMA system in all three array structures, especially in the UCA structure due to the smaller spacing of the array element in a circular shape, resulting in symmetric mutual coupling from both sides of the circle. On the other hand, it is also shown that compensating the mutual coupling effect by the MOM technique in the case of unknown MC or matrix inversion in the case of known MC significantly improves the system sum-rate in all scenarios.

Robust Weighted Subspace Fitting for DOA Estimation via Block Sparse Recovery

In this letter, a novel robust block sparse recovery algorithm by using the weighted subspace fitting (WSF) is proposed to deal with the direction-of-arrival (DOA) problem under the condition of unknown mutual coupling. Firstly, a novel block sparse representation signal model based on the WSF is established to settle the effect of unknown mutual coupling. Then, the sparse constraint problem is investigated, and a regularization criterion between the sparsity penalty and subspace fitting error is given. Finally, the DOA estimation problem can be converted into a block sparse recovery problem. Some experimental results are carried out to prove the performance of proposed method in the case of unknown mutual coupling.

DOA Estimation Algorithm for Strictly Noncircular Sources With Unknown Mutual Coupling

In this letter, an ESPRIT-based algorithm is proposed to handle the direction of arrival (DOA) estimation problem for strictly noncircular sources with unknown mutual coupling. At the cost of auxiliary elements, the structure of original mutual coupling matrix is compensated, and the unknown mutual coupling coefficients are separated from the distorted steering vector to obtain the ideal steering vector. Based on noncircularity, an extended received data is constructed by combining the received data and its conjugate counterpart. After that, ESPRIT framework is applied to the extended received data, and its key point is to utilize the diagonal relationship between the transformed manifold and the ideal manifold. Simulation results show that the proposed algorithm achieves larger virtual array aperture and comparable accuracy.

A Novel Block Sparse Reconstruction Method for DOA Estimation With Unknown Mutual Coupling

In this letter, we consider the direction-of-arrival (DOA) estimation in the presence of unknown mutual coupling in application to uniform linear arrays (ULAs). A novel method is proposed that treats the DOA estimation as a block sparse signal reconstruction problem with a modified array manifold matrix which utilizes the information of entire array output. A block smoothed $\ell _{0}$ norm approximation technique is introduced to solve the problem. Finally, an iterative proximal algorithm is proposed. Simulation results are presented that show the superiority of the proposed method against other known techniques.

DOA Estimation Under Mutual Coupling of Uniform Linear Arrays Using Sparse Reconstruction

A novel sparse reconstruction method is developed for direction of arrival (DOA) estimation in the presence of unknown mutual coupling of uniform linear arrays. In the proposed method, a sparse representation for single measurement vector (SMV) is first derived. Then, it is shown that the problem size can be reduced by a linear transformation to eliminate the redundant components in the SMV. Finally, by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix, a reweighted ${\mathcal {\ell }_{1}}$ -norm minimization subject to an error-constrained ${\mathcal {\ell }_{2}}$ -norm is introduced to determine the DOA estimates without mutual coupling compensation, further enhancing the sparsity and providing a robustness against the noise. Simulation results demonstrate the superiority of the proposed method over its state-of-the-art counterparts.

Sparse‐based direction finding in MIMO radar with mutual coupling

In this study, an estimation problem for target directions in the multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling is considered. To exploit the target sparsity in the spatial domain, a compressed sensing-based method using multiple measure vectors is proposed to reconstruct the sparse targets and estimate the target directions and scattering coefficients. Additionally, we also propose an iterative method to estimate the mutual coupling matrices after obtaining the target information, where the sub-gradients are derived theoretically to update the mutual coupling matrices. Simulation results show that compared with the exiting methods, the better estimation performance for both target directions and mutual coupling matrices can be achieved by the proposed method.

Block rank sparsity-aware DOA estimation with large-scale arrays in the presence of unknown mutual coupling

With the development of massive multiple-input mutiple-output (MIMO) technique, high-resolution direction-of-arrival (DOA) estimation has attracted great attention. A novel sparse signal reconstruction method based on the inherent block rank sparsity of the sub-matrix is proposed for high resolution DOA estimation with large-scale arrays under the condition of unknown mutual coupling. In the proposed method, by taking advantage of the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM), a novel block representation model is firstly formulated by parameterizing the steering vector. Then, exploiting the inherent block sparsity characteristics of the sub-matrix, a reweighted nuclear norm minimization algorithm is proposed to reconstruct the sparse matrix, in which the weighted matrix is designed by using the spectrum of MUSIC-Like algorithm. Finally, the DOAs are achieved by searching the non-zeros blocks of the recovered matrix. The proposed method not only makes full use of the block rank sparsity characteristics of the sub-matrix and weighted matrix for enhancing the sparse solution, but also avoids the array aperture loss. Thus, the proposed method has superior estimation performance than the state-of-the-art algorithms under the condition of unknown mutual coupling. Especially, in the case of large-scale antennas, the advantage of the proposed method is more obvious. Some computer simulation results are performed to verify the advantage of our proposed method.

Classification and Localization of Mixed Sources after Blind Calibration of Unknown Mutual Coupling

In order to deal with the problem of passive mixed source localization under unknown mutual coupling, the authors propose an effective algorithm. This algorithm provides array blind calibration as well as classification and localization of mixed sources in this paper. In practice, an ideal sensor array without the effects of unknown mutual coupling is rarely satisfied, which degrades the performance of most high-resolution algorithms. Firstly, the directions of arrival of far-field sources and the number of nonzero mutual coupling coefficients are estimated directly through the rank-reduction type method. Then, these estimates are adopted to reconstruct the mutual coupling matrix. In addition, the fourth-order cumulant technique is required to eliminate the Gauss colored noise effects caused by mutual coupling calibration of the raw received data vector. Finally, in an algebraic way, the results of rapid classification and localization of near-field sources are obtained without any spectral search. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

Joint Space and Time Processing for Unknown Mutual Coupling Blind Calibration and Mixed Sources Identification Using Uniform Circular Array

In classification and localization of mixed far-field and near-field sources, the unknown mutual coupling degrades the performance of most high-resolution algorithms. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. This paper proposes an effective algorithm of mixed sources identification using uniform circular array under unknown mutual coupling. Firstly, according to rank reduction and joint space–time processing, the directions of arrival of far-field sources is estimated directly without mutual coupling elimination. Addition, the joint space–time processing can improve the estimation results in the case of low signal noise ratio of incoming signal sources and small number of snapshots. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through spatial search after mutual coupling effects and far-field components elimination. The proposed algorithm is described in detail, and its behavior is illustrated by numerical examples.

Extended reduced‐rank joint estimation of direction of arrival with mutual coupling for coherent signals

AbstractThis paper proposes an extended method for direction‐of‐arrival (DOA) estimation with unknown mutual coupling for coherent signals. The proposed method employs the forward/backward spatial smoothing to decorrelate the coherent signals in the process of rank reduction with the joint iterative subspace optimization approach. Then, the autocalibration of mutual coupling is performed based on the Capon's minimum variance criterion. Performance of the proposed method is compared with the state of the art algorithms in terms of DOA root‐mean‐square error and in terms of probability of successful estimation, where successful estimation is the case when the average of absolute DOA estimation error is within 2.5°. It is shown that the proposed method has a similar performance as the state of the art autocalibration methods while offering a lower computational complexity.

Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling

Classification and Localization of Mixed Sources Using Uniform Circular Array under Unknown Mutual Coupling

Sparse off-grid DOA estimation method with unknown mutual coupling effect

In the array systems, the mutual coupling effect between antennas degrades the estimation performance of direction of arrival (DOA) significantly. In this paper, the DOA estimation problem in a uniform linear array (ULA) is investigated, and a novel system model is formulated by exploiting the mutual coupling effect and the signal sparsity in the spatial domain. In the traditionally compressed sensing (CS)-based system model, the spatial domain is discretized into grids, and the off-grid gap limits the further improvement of the estimation performance. Therefore, the novel system model is proposed by considering both the off-grid problem and the unknown mutual coupling effect. Moreover, a novel algorithm, named sparse off-grid DOA estimation with unknown mutual coupling effect (SODMC), is proposed and updates the unknown parameters including the DOA, the mutual coupling vector, and the off-grid vector iteratively. Simulation results show that compared with state-of-the-art methods, the proposed SODMC algorithm improves the DOA estimation performance significantly in the scenario with unknown mutual coupling effect.

Sparse-Based Millimeter Wave Channel Estimation With Mutual Coupling Effect

The imperfection of antenna array degrades the communication performance in the millimeter wave (mmWave) communication system. In this paper, the problem of channel estimation for the mmWave communication system is investigated, and the unknown mutual coupling (MC) effect between antennas is considered. By exploiting the channel sparsity in the spatial domain with mmWave frequency bands, the problem of channel estimation is converted into that of sparse reconstruction. The MC effect is described by a symmetric Toeplitz matrix, and the sparse-based mmWave system model with MC coefficients is formulated. Then, a two-stage method is proposed by estimating the sparse signals and MC coefficients iteratively. Simulation results show that the proposed method can significantly improve the channel estimation performance in the scenario with unknown MC effect and the estimation performance for both direction of arrival (DOA) and direction of departure (DoD) can be improved by about 8 dB by reducing the MC effect about 4 dB.